101-0139-00L Scientific Machine and Deep Learning for Design and Construction in Civil Engineering
Semester | Herbstsemester 2023 |
Dozierende | M. A. Kraus, D. Griego |
Periodizität | jährlich wiederkehrende Veranstaltung |
Lehrsprache | Englisch |
Lehrveranstaltungen
Nummer | Titel | Umfang | Dozierende | ||||
---|---|---|---|---|---|---|---|
101-0139-00 G | Scientific Machine and Deep Learning for Design and Construction in Civil Engineering 14-16 theory 16-18 group work | 4 Std. |
| M. A. Kraus, D. Griego |
Katalogdaten
Kurzbeschreibung | This course will present methods of scientific machine and deep learning (ML / DL) for applications in design and construction in civil engineering. After providing proper background on ML and the scientific ML (SciML) track, several applications of SciML together with their computational implementation during the design and construction process of the built environment are examined. |
Lernziel | This course aims to provide graduate level introduction into Machine and especially scientific Machine Learning for applications in the design and construction phases of projects from civil engineering. Upon completion of the course, the students will be able to: 1. understand main ML background theory and methods 2. assess a problem and apply ML and DL in a computational framework accordingly 3. Incorporating scientific domain knowledge in the SciML process 4. Define, Plan, Conduct and Present a SciML project |
Inhalt | The course will include theory and algorithms for SciML, programming assignments, as well as a final project assessment. The topics to be covered are: 1. Fundamentals of Machine and Deep Learning (ML / DL) 2. Incorporation of Domain Knowledge into ML and DL 3. ML training, validation and testing pipelines for academic and research projects A comprehensive series of computer/lab exercises and in-class demonstrations will take place, providing a "hands-on" feel for the course topics. |
Skript | The course script is composed by lecture slides, which are available online and will be continuously updated throughout the duration of the course. |
Literatur | Suggested Reading: Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong Mathematics for Machine Learning K. Murphy. Machine Learning: a Probabilistic Perspective. MIT Press 2012 C. Bishop. Pattern Recognition and Machine Learning. Springer, 2007 S. Guido, A. Müller: Introduction to machine learning with python. O'Reilly Media, 2016 O. Martin: Bayesian analysis with python. Packt Publishing Ltd, 2016 |
Voraussetzungen / Besonderes | Familiarity with MATLAB and / or Python is advised. |
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird) | |
Leistungskontrolle als Semesterkurs | |
ECTS Kreditpunkte | 3 KP |
Prüfende | M. A. Kraus, D. Griego |
Form | Semesterendprüfung |
Prüfungssprache | Englisch |
Repetition | Die Leistungskontrolle wird nur am Semesterende nach der Lerneinheit angeboten. Die Repetition ist nur nach erneuter Belegung möglich. |
Prüfungsmodus | mündlich 5 Minuten |
Zusatzinformation zum Prüfungsmodus | Mandatory final project examination: The final grade will be obtained based on 1. Project presentation (15 min) and public Q&A (5 min). Projects are conducted in pairs. (50% of the final grade). This compulsory continuous performance assessment task need not be passed on its own; it is awarded a grade which counts proportionally towards the total course unit grade. 2. Followed by a non-public individual oral examination (5 min). (50% of the final grade). |
Lernmaterialien
Keine öffentlichen Lernmaterialien verfügbar. | |
Es werden nur die öffentlichen Lernmaterialien aufgeführt. |
Gruppen
Keine Informationen zu Gruppen vorhanden. |
Einschränkungen
Plätze | Maximal 20 |
Warteliste | Bis 29.09.2023 |